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反向散射NOMA赋能的混合多播-单播协作传输方案

阔永红 薛彦文 吕璐 贺冰涛 陈健

阔永红, 薛彦文, 吕璐, 贺冰涛, 陈健. 反向散射NOMA赋能的混合多播-单播协作传输方案[J]. 电子与信息学报. doi: 10.11999/JEIT230672
引用本文: 阔永红, 薛彦文, 吕璐, 贺冰涛, 陈健. 反向散射NOMA赋能的混合多播-单播协作传输方案[J]. 电子与信息学报. doi: 10.11999/JEIT230672
KUO Yonghong, XUE Yanwen, LÜ Lu, HE Bingtao, CHEN Jian. Backscatter-NOMA Enabled Hybrid Multicast-Unicast Cooperative Transmission Scheme[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230672
Citation: KUO Yonghong, XUE Yanwen, LÜ Lu, HE Bingtao, CHEN Jian. Backscatter-NOMA Enabled Hybrid Multicast-Unicast Cooperative Transmission Scheme[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230672

反向散射NOMA赋能的混合多播-单播协作传输方案

doi: 10.11999/JEIT230672
基金项目: 国家自然科学基金(62271368, 61971320, 62201421),中国博士后科学基金(BX20190264, 2019M650258),陕西省重点研发计划(2023-YBGY-041),陕西省自然科学基础研究计划(2021JQ-206),广东省基础与应用基础研究基金(2020A1515110084)
详细信息
    作者简介:

    阔永红:女,教授,研究方向为无线通信与信号处理

    薛彦文:男,硕士生,研究方向为协作非正交多址接入、反向散射通信

    吕璐:男,副教授,研究方向为物理层安全、隐蔽通信等

    贺冰涛:男,讲师,研究方向为非正交多址接入、协作通信等

    陈健:男,教授,研究方向为无线网络虚拟化、非正交多址接入

    通讯作者:

    贺冰涛 bthe@xidian.edu.cn

  • 中图分类号: TN92

Backscatter-NOMA Enabled Hybrid Multicast-Unicast Cooperative Transmission Scheme

Funds: The National Natural Science Foundation of China (62271386, 61971320, 62201421), China Postdoctoral Science Foundation Project (BX20190264, 2019M650258), The Key R&D Program Project of Shaanxi Province (2023-YBGY-041), The Natural Science Basic Research Plan of Shaanxi Province (2021JQ-206), Guangdong Basic and Applied Basic Research Foundation (2020A1515110084)
  • 摘要: 针对协作中继通信系统频谱效率低和链路利用率低的问题,面向多播、单播业务共存场景,该文提出一种反向散射NOMA赋能的混合多播-单播协作传输方案。机会式选择一个多播用户作为协作节点,将其接收信号的一部分功率用于自身解码,剩余功率反向散射以增强其余用户的接收质量。为提升系统性能,通过联合优化基站功率分配系数、协作用户反向散射系数和协作节点选择变量,在保障多播服务质量的前提下,实现单播用户最小可达速率的最大化。为解决上述高度非凸联合优化问题,该文设计一种协作用户选择准则并提出了一种迭代算法来获取原问题的最优解。仿真结果验证了所提迭代算法的快速收敛性,相较于传统非协作传输方案,所提方案可将单播用户最小可达速率提升11.5%,有效保证多业务服务质量。
  • 图  1  BS-NOMA协作传输系统

    图  2  算法收敛性分析

    图  3  多播用户不同服务质量需求下的性能比较

    图  4  不同用户数量的性能比较

    图  5  不同传输方案的性能比较

    图  6  不同资源分配方案的性能比较

    图  7  不同协作用户选择方案性能对比

    1  基于连续凸近似的优化算法

     1. 初始化:给定$n = {\text{1}}$, $\varDelta = 1$, ${{\varGamma }}_1^{\left( 0 \right)} = {{\varGamma }}_2^{\left( 0 \right)} = \cdots = {{\varGamma }}_N^{\left( 0 \right)} = 10$,
     ${{v}}_1^{\left( 0 \right)} = {{v}}_2^{\left( 0 \right)} = \cdots = {{v}}_N^{\left( 0 \right)} = 1.2$, $\lambda _1^{\left( 0 \right)} = \lambda _2^{\left( 0 \right)} = \cdots = \lambda _N^{\left( 0 \right)} = 1.5$,
     $\tau = 0.001$, ${R^{{\text{(0)}}}} = 0$。
     2. while $\varDelta > \tau $ do
     3. 求解问题P2,得到${R^{{\text{(}}n{\text{)}}}}$, ${{\varGamma }}_1^{\left( n \right)},{{\varGamma }}_2^{\left( n \right)} ,\cdots, {{\varGamma }}_N^{\left( n \right)}$和
     ${\text{v}}_1^{\left( n \right)},{\text{v}}_1^{\left( n \right)}, \cdots ,{\text{v}}_N^{\left( n \right)}$;
     4. $n \leftarrow n + 1$;
     5. 更新$\varDelta = \left| {{R^{{\text{(}}n{\text{)}}}} - {R^{{\text{(}}n - 1{\text{)}}}}} \right|$;
     6. 根据式3-34更新$\lambda _1^{\left( n \right)},\lambda _2^{\left( n \right)}, \cdots ,\lambda _N^{\left( n \right)}$;
     7. end
     8. 输出:最优速率${R^*}$。
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-07-07
  • 修回日期:  2024-03-25
  • 网络出版日期:  2024-04-08

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